mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-09-22 01:25:06 +02:00
78938ec037
## Summary Builds on #7586. Adds `snapshot_frequency: int | None` to `DeltaChannel`, letting users trade storage for bounded read depth. Also promotes `channels/_delta.py` from private to public (`channels/delta.py`). ### How it works Every Nth **pregel step**, `create_checkpoint` writes a `_DeltaSnapshot` blob instead of `DELTA_SENTINEL`. The ancestor walk in `_get_channel_writes_history` terminates at the snapshot rather than walking the full chain, bounding replay to at most N steps. Snapshots are **eager**: fired even on steps where the channel had no write (via a `get_next_version` version bump), so the depth bound holds unconditionally — no risk of the cadence drifting if a channel happens to be silent at a snapshot step. ### Storage formula | Mode | Blob storage | Read depth | |------|-------------|------------| | `snapshot_frequency=None` (pure delta) | O(N) — sentinels only | O(N) steps | | `snapshot_frequency=K` | O(N²/K) — periodic snapshots of growing size | O(K) steps | | add_messages / BinOp | O(N²) — full blob every step | O(1) | At N turns with ~400 char/msg messages, total snapshot storage ≈ N²/(2K) × avg_msg_size, since each snapshot blob grows linearly with accumulated messages. ### Key design decisions - **Step-based**: `snapshot_frequency=K` means "snapshot every K pregel steps." `create_checkpoint` has the step number; the channel itself doesn't need to track writes. - **Eager**: version-bumped via `get_next_version` even on non-write steps so `put()` always stores the blob. - **`_DeltaSnapshot` NamedTuple + msgpack ext type** (`EXT_DELTA_SNAPSHOT = 7`): serde type tag dispatches in `from_checkpoint` — no dict key inspection, no collision risk. - **`from_checkpoint` semantics**: `_DeltaSnapshot` → restore value directly (no replay needed); `DELTA_SENTINEL` / `MISSING` → replay from ancestor writes; plain value → pre-migration BinOp blob. - **InMemorySaver and PostgresSaver updated**: `_get_channel_writes_history` collects the snapshot ancestor's pending_writes before terminating (they encode the *next* step's transition, unlike pre-delta migration blobs which subsume their own writes). - **`snapshot_frequency=None`** is the pure-delta default (replaces `math.inf`). ### Benchmark results (InMemory, ~400 char/msg) **Storage** | turns | ctx | freq=1 | freq=5 | freq=10 | freq=50 | freq=inf | |------:|----:|-------:|-------:|--------:|--------:|---------:| | 50 | ~10K tok | 5.9 MB | 1.2 MB | 601.3 KB | 119.8 KB | 29.5 KB | | 100 | ~20K tok | 23.7 MB | 4.8 MB | 2.4 MB | 475.8 KB | 58.4 KB | | 200 | ~40K tok | 94.6 MB | 19.0 MB | 9.5 MB | 1.9 MB | 116.4 KB | | 500 | ~100K tok | 591.5 MB | 118.4 MB | 59.2 MB | 11.8 MB | 290.3 KB | **Read latency** (avg of 5 `get_state` calls) | turns | ctx | freq=1 | freq=5 | freq=10 | freq=50 | freq=inf | |------:|----:|-------:|-------:|--------:|--------:|---------:| | 50 | ~10K tok | 0.4ms | 0.4ms | 0.7ms | 0.9ms | 1.8ms | | 100 | ~20K tok | 0.7ms | 0.9ms | 1.0ms | 1.7ms | 5.7ms | | 200 | ~40K tok | 1.5ms | 1.7ms | 4.5ms | 3.7ms | 20.1ms | | 500 | ~100K tok | 3.6ms | 4.2ms | 4.4ms | 9.0ms | 110.3ms | **Per-invoke write latency** | turns | ctx | freq=1 | freq=5 | freq=10 | freq=50 | freq=inf | |------:|----:|-------:|-------:|--------:|--------:|---------:| | 50 | ~10K tok | 1.5ms | 1.1ms | 1.1ms | 1.3ms | 1.7ms | | 100 | ~20K tok | 2.5ms | 1.6ms | 1.5ms | 1.7ms | 3.3ms | | 200 | ~40K tok | 3.4ms | 2.3ms | 2.2ms | 2.5ms | 8.3ms | | 500 | ~100K tok | 6.2ms | 4.2ms | 3.6ms | 4.1ms | 39.2ms | ## Test plan - [x] `make format` / `make lint` clean across `langgraph`, `checkpoint`, `checkpoint-postgres` - [x] `tests/test_channels.py` — 37 passing including step-based and eager-snapshot tests - [x] `tests/test_delta_channel_migration.py` — all passing - [x] Full suite: 1387 passing, 6 pre-existing failures unrelated to this branch --------- Co-authored-by: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
Low-level orchestration framework for building stateful agents.
Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
pip install -U langgraph
If you're looking to quickly build agents with LangChain's create_agent (built on LangGraph), check out the LangChain Agents documentation.
Note
Looking for the JS/TS library? Check out LangGraph.js and the JS docs.
Why use LangGraph?
LangGraph provides low-level supporting infrastructure for any long-running, stateful workflow or agent:
- Durable execution — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- Human-in-the-loop — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- Comprehensive memory — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- Debugging with LangSmith — Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- Production-ready deployment — Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
Tip
For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
LangGraph ecosystem
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
To improve your LLM application development, pair LangGraph with:
- Deep Agents (new!) – Build agents that can plan, use subagents, and leverage file systems for complex tasks.
- LangChain – Provides integrations and composable components to streamline LLM application development.
- LangSmith – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- LangSmith Deployment – Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams – and iterate quickly with visual prototyping in LangSmith Studio.
Documentation
- docs.langchain.com – Comprehensive documentation, including conceptual overviews and guides
- reference.langchain.com/python/langgraph – API reference docs for LangGraph packages
- LangGraph Quickstart – Get started building with LangGraph
- Chat LangChain – Chat with the LangChain documentation and get answers to your questions
Discussions: Visit the LangChain Forum to connect with the community and share all of your technical questions, ideas, and feedback.
Additional resources
- Guides – Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- LangChain Academy – Learn the basics of LangGraph in our free, structured course.
- Case studies – Hear how industry leaders use LangGraph to ship AI applications at scale.
- Contributing Guide – Learn how to contribute to LangChain projects and find good first issues.
- Code of Conduct – Our community guidelines and standards for participation.
Acknowledgements
LangGraph is inspired by Pregel and Apache Beam. The public interface draws inspiration from NetworkX. LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.